OpenRaiser/Intern-Atlas huggingface dataset:
A System for Clock Synchronization in an Internet of Things
Venue: arXiv cs.NI 2018
Year: 2018

Synchronizing clocks on Internet of Things (IoT) devices is important for applications such as monitoring and real time control. In this paper, we describe a system for clock synchronization in IoT devices that is designed to be scalable, flexibly accommodate diverse hardware, and maintain tight synchronization over a range of operating conditions. We begin by examining clock drift on two standard IoT prototyping platforms. We observe clock drift on the order of seconds over relatively short time periods, as well as poor clock rate stability, each of which make standard synchronization protocols ineffective. To address this problem, we develop a synchronization system, which includes a lightweight client, a new packet exchange protocol called SPoT and a scalable reference server. We evaluate the efficacy of our system over a range of configurations, operating conditions and target platforms. We find that SPoT performs synchronization 22x and 17x more accurately than MQTT and SNTP, respectively, at high noise levels, and maintains a clock accuracy of within ~15ms at various noise levels. Finally, we report on the scalability of our server implementation through microbenchmark and wide area experiments, which show that our system can scale to support large numbers of clients efficiently.
Joint Optimization of Computation and Communication Power in Multi-user Massive MIMO Systems
Venue: arXiv cs.NI 2018
Year: 2018

With the growing interest in the deployment of massive multiple-input-multiple-output (MIMO) systems and millimeter wave technology for fifth generation (5G) wireless systems, the computation power to the total power consumption ratio is expected to increase rapidly due to high data traffic processing at the baseband unit. Therefore in this paper, a joint optimization problem of computation and communication power is formulated for multi-user massive MIMO systems with partially-connected structures of radio frequency (RF) transmission systems. When the computation power is considered for massiv MIMO systems, the results of this paper reveal that the energy efficiency of massive MIMO systems decreases with increasing the number of antennas and RF chains, which is contrary with the conventional energy efficiency analysis results of massive MIMO systems, i.e., only communication power is considered. To optimize the energy efficiency of multi-user massive MIMO systems, an upper bound on energy efficiency is derived. Considering the constraints on partially-connected structures, a suboptimal solution consisting of baseband and RF precoding matrices is proposed to approach the upper bound on energy efficiency of multi-user massive MIMO systems. Furthermore, an oPtimized Hybrid precOding with computation and commuNication powEr (PHONE) algorithm is developed to realize the joint optimization of computation and communication power. Simulation results indicate that the proposed algorithm improves energy and cost efficiencies and the maximum power saving is achieved by 76.59\% for multi-user massive MIMO systems with partially-connected structures.
Conditional probability calculation using restricted Boltzmann machine with application to system identification
Venue: arXiv cs.SY 2018
Year: 2018

There are many advantages to use probability method for nonlinear system identification, such as the noises and outliers in the data set do not affect the probability models significantly; the input features can be extracted in probability forms. The biggest obstacle of the probability model is the probability distributions are not easy to be obtained. In this paper, we form the nonlinear system identification into solving the conditional probability. Then we modify the restricted Boltzmann machine (RBM), such that the joint probability, input distribution, and the conditional probability can be calculated by the RBM training. Binary encoding and continue valued methods are discussed. The universal approximation analysis for the conditional probability based modelling is proposed. We use two benchmark nonlinear systems to compare our probability modelling method with the other black-box modeling methods. The results show that this novel method is much better when there are big noises and the system dynamics are complex.
GP-RVM: Genetic Programing-based Symbolic Regression Using Relevance Vector Machine
Venue: arXiv cs.NE 2018
Year: 2018

This paper proposes a hybrid basis function construction method (GP-RVM) for Symbolic Regression problem, which combines an extended version of Genetic Programming called Kaizen Programming and Relevance Vector Machine to evolve an optimal set of basis functions. Different from traditional evolutionary algorithms where a single individual is a complete solution, our method proposes a solution based on linear combination of basis functions built from individuals during the evolving process. RVM which is a sparse Bayesian kernel method selects suitable functions to constitute the basis. RVM determines the posterior weight of a function by evaluating its quality and sparsity. The solution produced by GP-RVM is a sparse Bayesian linear model of the coefficients of many non-linear functions. Our hybrid approach is focused on nonlinear white-box models selecting the right combination of functions to build robust predictions without prior knowledge about data. Experimental results show that GP-RVM outperforms conventional methods, which suggest that it is an efficient and accurate technique for solving SR. The computational complexity of GP-RVM scales in $O( M^{3})$, where $M$ is the number of functions in the basis set and is typically much smaller than the number $N$ of training patterns.
Multi-cell Hybrid Millimeter Wave Systems: Pilot Contamination and Interference Mitigation
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we investigate the system performance of a multi-cell multi-user (MU) hybrid millimeter wave (mmWave) communications in a multiple-input multiple-output (MIMO) network. Due to the reuse of pilot symbols among different cells, the performance of channel estimation is expected to be degraded by pilot contamination, which is considered as a fundamental performance bottleneck of conventional multicell MU massive MIMO networks. To analyze the impact of pilot contamination to the system performance, we first derive the closed-form approximation of the normalized mean squared error (MSE) of the channel estimation algorithm proposed in [2] over Rician fading channels. Our analytical and simulation results show that the channel estimation error incurred by the impact of pilot contamination and noise vanishes asymptotically with an increasing number of antennas equipped at each radio frequency (RF) chain at the desired BS. Furthermore, by adopting zero-forcing (ZF) precoding in each cell for downlink transmission, we derive a tight closed-form approximation of the average achievable rate per user. Our results unveil that the intra-cell interference and inter-cell interference caused by pilot contamination over Rician fading channels can be mitigated effectively by simply increasing the number of antennas equipped at the desired BS.
Multi-Source Neural Machine Translation with Missing Data
Venue: arXiv cs.CL 2018
Year: 2018

Multi-source translation is an approach to exploit multiple inputs (e.g. in two different languages) to increase translation accuracy. In this paper, we examine approaches for multi-source neural machine translation (NMT) using an incomplete multilingual corpus in which some translations are missing. In practice, many multilingual corpora are not complete due to the difficulty to provide translations in all of the relevant languages (for example, in TED talks, most English talks only have subtitles for a small portion of the languages that TED supports). Existing studies on multi-source translation did not explicitly handle such situations. This study focuses on the use of incomplete multilingual corpora in multi-encoder NMT and mixture of NMT experts and examines a very simple implementation where missing source translations are replaced by a special symbol <NULL>. These methods allow us to use incomplete corpora both at training time and test time. In experiments with real incomplete multilingual corpora of TED Talks, the multi-source NMT with the <NULL> tokens achieved higher translation accuracies measured by BLEU than those by any one-to-one NMT systems.
Fault-Tolerant Control of Linear Quantum Stochastic Systems
Venue: arXiv cs.SY 2018
Year: 2018

In quantum engineering, faults may occur in a quantum control system, which will cause the quantum control system unstable or deteriorate other relevant performance of the system. This note presents an estimator-based fault-tolerant control design approach for a class of linear quantum stochastic systems subject to fault signals. In this approach, the fault signals and some commutative components of the quantum system observables are estimated, and a fault-tolerant controller is designed to compensate the effect of the fault signals. Numerical procedures are developed for controller design and an example is presented to demonstrate the proposed design approach.
Rust Distilled: An Expressive Tower of Languages
Venue: arXiv cs.PL 2018
Year: 2018

Rust represents a major advancement in production programming languages because of its success in bridging the gap between high-level application programming and low-level systems programming. At the heart of its design lies a novel approach to ownership that remains highly programmable. In this talk, we will describe our ongoing work on designing a formal semantics for Rust that captures ownership and borrowing without the details of lifetime analysis. This semantics models a high-level understanding of ownership and as a result is close to source-level Rust (but with full type annotations) which differs from the recent RustBelt effort that essentially models MIR, a CPS-style IR used in the Rust compiler. Further, while RustBelt aims to verify the safety of unsafe code in Rust's standard library, we model standard library APIs as primitives, which is sufficient to reason about their behavior. This yields a simpler model of Rust and its type system that we think researchers will find easier to use as a starting point for investigating Rust extensions. Unlike RustBelt, we aim to prove type soundness using progress and preservation instead of a Kripke logical relation. Finally, our semantics is a family of languages of increasing expressive power, where subsequent levels have features that are impossible to define in previous levels. Following Felleisen, expressive power is defined in terms of observational equivalence. Separating the language into different levels of expressive power should provide a framework for future work on Rust verification and compiler optimization.
Evaluating surgical skills from kinematic data using convolutional neural networks
Venue: arXiv cs.CV 2018
Year: 2018

The need for automatic surgical skills assessment is increasing, especially because manual feedback from senior surgeons observing junior surgeons is prone to subjectivity and time consuming. Thus, automating surgical skills evaluation is a very important step towards improving surgical practice. In this paper, we designed a Convolutional Neural Network (CNN) to evaluate surgeon skills by extracting patterns in the surgeon motions performed in robotic surgery. The proposed method is validated on the JIGSAWS dataset and achieved very competitive results with 100% accuracy on the suturing and needle passing tasks. While we leveraged from the CNNs efficiency, we also managed to mitigate its black-box effect using class activation map. This feature allows our method to automatically highlight which parts of the surgical task influenced the skill prediction and can be used to explain the classification and to provide personalized feedback to the trainee.
Embedding Transfer for Low-Resource Medical Named Entity Recognition: A Case Study on Patient Mobility
Venue: arXiv cs.CL 2018
Year: 2018

Functioning is gaining recognition as an important indicator of global health, but remains under-studied in medical natural language processing research. We present the first analysis of automatically extracting descriptions of patient mobility, using a recently-developed dataset of free text electronic health records. We frame the task as a named entity recognition (NER) problem, and investigate the applicability of NER techniques to mobility extraction. As text corpora focused on patient functioning are scarce, we explore domain adaptation of word embeddings for use in a recurrent neural network NER system. We find that embeddings trained on a small in-domain corpus perform nearly as well as those learned from large out-of-domain corpora, and that domain adaptation techniques yield additional improvements in both precision and recall. Our analysis identifies several significant challenges in extracting descriptions of patient mobility, including the length and complexity of annotated entities and high linguistic variability in mobility descriptions.
Scalable Natural Gradient Langevin Dynamics in Practice
Venue: arXiv cs.LG 2018
Year: 2018

Stochastic Gradient Langevin Dynamics (SGLD) is a sampling scheme for Bayesian modeling adapted to large datasets and models. SGLD relies on the injection of Gaussian Noise at each step of a Stochastic Gradient Descent (SGD) update. In this scheme, every component in the noise vector is independent and has the same scale, whereas the parameters we seek to estimate exhibit strong variations in scale and significant correlation structures, leading to poor convergence and mixing times. We compare different preconditioning approaches to the normalization of the noise vector and benchmark these approaches on the following criteria: 1) mixing times of the multivariate parameter vector, 2) regularizing effect on small dataset where it is easy to overfit, 3) covariate shift detection and 4) resistance to adversarial examples.
Training Faster by Separating Modes of Variation in Batch-normalized Models
Venue: arXiv cs.LG 2018
Year: 2018

Batch Normalization (BN) is essential to effectively train state-of-the-art deep Convolutional Neural Networks (CNN). It normalizes inputs to the layers during training using the statistics of each mini-batch. In this work, we study BN from the viewpoint of Fisher kernels. We show that assuming samples within a mini-batch are from the same probability density function, then BN is identical to the Fisher vector of a Gaussian distribution. That means BN can be explained in terms of kernels that naturally emerge from the probability density function of the underlying data distribution. However, given the rectifying non-linearities employed in CNN architectures, distribution of inputs to the layers show heavy tail and asymmetric characteristics. Therefore, we propose approximating underlying data distribution not with one, but a mixture of Gaussian densities. Deriving Fisher vector for a Gaussian Mixture Model (GMM), reveals that BN can be improved by independently normalizing with respect to the statistics of disentangled sub-populations. We refer to our proposed soft piecewise version of BN as Mixture Normalization (MN). Through extensive set of experiments on CIFAR-10 and CIFAR-100, we show that MN not only effectively accelerates training image classification and Generative Adversarial networks, but also reaches higher quality models.
Optimal Design of Process Flexibility for General Production Systems
Venue: arXiv cs.DS 2018
Year: 2018

Process flexibility is widely adopted as an effective strategy for responding to uncertain demand. Many algorithms for constructing sparse flexibility designs with good theoretical guarantees have been developed for balanced and symmetrical production systems. These systems assume that the number of plants equals the number of products, that supplies have the same capacity, and that demands are independently and identically distributed. In this paper, we relax these assumptions and consider a general class of production systems. We construct a simple flexibility design to fulfill $(1-ε)$-fraction of expected demand with high probability (w.h.p.) where the average degree is $O(\ln(1/ε))$. To motivate our construction, we first consider a natural weighted probabilistic construction from Chou et al. (2011) where the degree of each node is proportional to its expected capacity. However, this strategy is shown to be sub-optimal. To obtain an optimal construction, we develop a simple yet effective thresholding scheme. The analysis of our approach extends the classical analysis of expander graphs by overcoming several technical difficulties. Our approach may prove useful in other applications that require expansion properties of graphs with non-uniform degree sequences.
Coverage Probability of 3D Mobile UAV Networks
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we consider a network of multiple unmanned aerial vehicles (UAVs) where a given number of UAVs are placed at three-dimensional (3D) locations in a finite circular disk shaped region to serve a reference ground user equipment (UE) located at its center. Herein, a serving UAV is assumed to be located at fixed altitude which communicates with the reference UE. All the other UAVs in the network are designated as interfering UAVs to the UE and are assumed to have 3D mobility. To characterize the 3D UAV movement process, we hereby propose an effective 3D mobility model based on the mixed random waypoint mobility (RWPM) and uniform mobility (UM) models in the vertical and spatial directions. Further, considering the proposed 3D mobility model, we first characterize the interference received at reference UE, and then evaluate its coverage probability under Nakagami-m fading. We quantify the achievable performance gains for the ground UE under various system and channel conditions. Moreover, we corroborate our analytical results through simulations.
Asynchronous Downlink Massive MIMO Networks: A Stochastic Geometry Approach
Venue: arXiv cs.IT 2018
Year: 2018

Massive multiple-input multiple-output (MIMO) is recognized as a promising technology for the next generation of wireless networks because of its potential to increase the spectral efficiency. In initial studies of massive MIMO, the system has been considered to be perfectly synchronized throughout the entire cells. However, perfect synchronization may be hard to attain in practice. Therefore, we study a massive MIMO system whose cells are not synchronous to each other, while transmissions in a cell are still synchronous. We analyze an asynchronous downlink massive MIMO system in terms of the coverage probability and the ergodic rate by means of the stochastic geometry tool. For comparison, we also obtain the results for the synchronous systems. In addition, we investigate the effect of the uplink power control and the number of pilot symbols on the downlink ergodic rate, and we observe that there is an optimal value for the number of pilot symbols maximizing the downlink ergodic rate of a cell. Our results also indicate that, compared to the cases with synchronous transmission, the downlink ergodic rate is more sensitive to the uplink power control in the asynchronous mode.
Noise-adding Methods of Saliency Map as Series of Higher Order Partial Derivative
Venue: arXiv cs.LG 2018
Year: 2018

SmoothGrad and VarGrad are techniques that enhance the empirical quality of standard saliency maps by adding noise to input. However, there were few works that provide a rigorous theoretical interpretation of those methods. We analytically formalize the result of these noise-adding methods. As a result, we observe two interesting results from the existing noise-adding methods. First, SmoothGrad does not make the gradient of the score function smooth. Second, VarGrad is independent of the gradient of the score function. We believe that our findings provide a clue to reveal the relationship between local explanation methods of deep neural networks and higher-order partial derivatives of the score function.
3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents a fully automated atlas-based pancreas segmentation method from CT volumes utilizing 3D fully convolutional network (FCN) feature-based pancreas localization. Segmentation of the pancreas is difficult because it has larger inter-patient spatial variations than other organs. Previous pancreas segmentation methods failed to deal with such variations. We propose a fully automated pancreas segmentation method that contains novel localization and segmentation. Since the pancreas neighbors many other organs, its position and size are strongly related to the positions of the surrounding organs. We estimate the position and the size of the pancreas (localized) from global features by regression forests. As global features, we use intensity differences and 3D FCN deep learned features, which include automatically extracted essential features for segmentation. We chose 3D FCN features from a trained 3D U-Net, which is trained to perform multi-organ segmentation. The global features include both the pancreas and surrounding organ information. After localization, a patient-specific probabilistic atlas-based pancreas segmentation is performed. In evaluation results with 146 CT volumes, we achieved 60.6% of the Jaccard index and 73.9% of the Dice overlap.
Blockchain and Principles of Business Process Re-Engineering for Process Innovation
Venue: arXiv cs.SE 2018
Year: 2018

Blockchain has emerged as one of the most promising and revolutionary technologies in the past years. Companies are exploring implementation of use cases in hope of significant gains in efficiencies. However, to achieve the impact hoped for, it is not sufficient to merely replace existing technologies. The current business processes must also be redesigned and innovated to enable realization of hoped for benefits. This conceptual paper provides a theoretical contribution on how blockchain technology and smart contracts potentially can, within the framework of the seven principles of business process re-engineering (BPR), enable process innovations. In this paper, we analyze the BPR principles in light of their applicability to blockchain-based solutions. We find these principles to be applicable and helpful in understanding how blockchain technology could enable transformational redesign of current processes. However, the viewpoint taken, should be expanded from intra- to inter-organizational processes operating within an ecosystem of separate organizational entities. In such a blockchain powered ecosystem, smart contracts take on a pivotal role, both as repositories of data and executioner of activities.
Provably Secure Integration Cryptosystem on Non-Commutative Group
Venue: arXiv cs.CR 2018
Year: 2018

Braid group is a very important non-commutative group. It is also an important tool of quantum field theory, and has good topological properties. This paper focuses on the provable security research of cryptosystem over braid group, which consists of two aspects: One, we proved that the Ko's cryptosystem based on braid group is secure against chosen-plaintext-attack(CPA) which proposed in CRYPTO2000, while it dose not resist active attack. The other is to propose a new public key cryptosystem over braid group which is secure against adaptive chosen-ciphertext-attack(CCA2). Our proofs are based on random oracle models, under the computational conjugacy search assumption( the CCS assumption ). This kind of results have never been seen before.